Skip to main content
Glama

referencesource.org

by io.github.joelp22-maker

List reference datasets

list_datasets
Read-only

What reference datasets are available? Lists every published dataset with its title, record count, last-verified date and a one-sentence summary. Covers regulatory thresholds, version compatibility, certification registers, licensing rules and more. Pass query to list only the datasets matching a topic — that is much cheaper than reading the whole list. Use the returned slug with search_records or get_record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn each dataset's complete scope statement instead of the first sentence. Off by default: the full text is roughly four times the size and is rarely needed to choose a dataset.
queryNoOptional topic filter, e.g. "boiler", "tariff" or "loan limit". Case-insensitive substring, matched against each dataset's slug, title and full description. Omit it to list every dataset.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / full
      Added value: +{
+  "description": "Return each dataset's complete scope statement instead of the first sentence. Off by default: the full text is roughly four times the size and is rarely needed to choose a dataset.",
+  "type": "boolean"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Optional topic filter, e.g. \"boiler\", \"tariff\" or \"loan limit\". Case-insensitive substring, matched against each dataset's slug, title and full description. Omit it to list every dataset.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The readOnlyHint and destructiveHint annotations already establish that this is a safe read operation. The description adds useful behavioral context: the output fields, the breadth of coverage, a performance note that query filtering is 'much cheaper' than the full list, and the fact that slugs are intended for downstream calls.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences carry exactly the needed information: a question-shaped opener that frames purpose, a compact list of output fields and coverage, and a closing routing note. No redundant phrases or restatements of the title; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple catalog-listing tool with zero required parameters, the description is complete: it names the output fields, explains both parameters' effects, signals cost behavior, and tells the agent what to do with the returned slug. The absence of an output schema is mitigated by the explicit field enumeration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds a modest performance/scoping insight about the query parameter, but does not need to compensate for missing parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action with a distinct resource: it lists every published dataset and enumerates the exact returned fields (title, record count, last-verified date, summary). It also names the covered topic categories, and the closing sentence ties it to sibling tools, making it easy to distinguish from search_records and get_record.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly explains when to use the query parameter versus listing everything, and it explicitly points to the sibling tools search_records and get_record as follow-ups using the returned slug. It does not state an explicit 'when not to use this tool' condition, but the usage context is otherwise clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All referencesource.org toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a unique role: get_record retrieves by ID, list_datasets enumerates available datasets, search_records performs substring search within a dataset, and verify_quote checks claims against published records. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow the same verb_noun snake_case pattern (get_record, list_datasets, search_records, verify_quote), making the action and target clear and predictable.

Tool Count5/5

Four tools is an appropriate size for a focused reference/verification server. Each tool serves a distinct step in the workflow, and none are redundant or missing.

Completeness4/5

The tool set covers the core flow of discovering datasets, searching records, retrieving specific records, and verifying claims. Minor gaps exist (e.g., no explicit dataset detail endpoint), but the tools are sufficient for the server's stated purpose.

Resources